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exo_step2.R
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exo_step2.R
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#save.image(file = "~/TCGA/exosome_ccRCC/exoalldata.rds")
load("~/vip39/TCGA/exosome_ccRCC/exoalldata.rds")
setwd("~/TCGA/exosome_ccRCC/validation/")
load("~/TCGA/exosome_ccRCC/validation/exsomevalidation.rds")
load("~/TCGA/exosome_ccRCC/exoalldata.rds")
##开始构建模型
#lasso联合bostrap
#参考 生信作曲家课程
#install.packages('boot')
library(boot)
###########寻找预后相关的G蛋白偶联受体##############
library(survival) #引用包
pFilter= 0.05 #显著性过滤条件
library(limma) #引用包
# 数据处理
## 载入TCGA胃癌表达谱
# load('STAD_tpm.Rdata')
# gpr=read.table('G_protein.txt')
# gpr=as.data.frame(t(gpr))
# gpr=gpr$V1
# 获取GPR基因
gpr=finalgene
# GPR表达谱的差异分析
KIRC_tpm <- log(KIRC_tpm+1)
kirc.expr[1:4,1:4]
data=KIRC_tpm[rownames(KIRC_tpm) %in% gpr,]
## 肿瘤和正常样品
group=sapply(strsplit(colnames(data),"\\-"),"[",4)
group=sapply(strsplit(group,""),"[",1)
group_list=ifelse(group=="0",'tumor','normal')
group_list=factor(group_list,levels = c('normal','tumor'))
library(limma)
design=model.matrix(~ group_list)
fit=lmFit(data,design)
fit=eBayes(fit)
allDiff=topTable(fit,adjust='fdr',coef=2,number=Inf,p.value=0.05)
write.csv(allDiff,file ='allDiff.csv',quote = F)
## 更新data
data=data[rownames(allDiff),]
#删掉正常样品
group=sapply(strsplit(colnames(data),"\\-"),"[",4)
group=sapply(strsplit(group,""),"[",1)
group=gsub("2","1",group)
data=data[,group==0]
## 去除在大多数样本中都表达为0的基因
keep <- rowSums(data>0) >= floor(0.75*ncol(data))
table(keep)
## 30
data<- data[keep,]
data=as.data.frame(t(data))
# 单因素寻找预后影响的GPR
## 读取生存数据
#suv=read.table('TCGA-STAD.survival.tsv',row.names = 1,header = T,check.names = F)
cli=dplyr::select(kirc.sinfo,'survival_time','fustat')
colnames(cli)=c("futime", "fustat")
## 数据合并并输出结果
data1 <- data%>%
rownames_to_column("ID")%>%
select(ID,everything())
data1$ID <- str_sub(data1$ID,1,12)
data1 <- data1[!duplicated(data1$ID),]
rownames(data1) <- data1$ID
data1 <- data1[,-1]
data <- data1
sameSample=intersect(row.names(data),row.names(cli))
data=data[sameSample,]
cli=cli[sameSample,]
#你需要牢记这样的结构，用于cox
out=cbind(cli,data)
out=cbind(id=row.names(out),out)
## 写出GPR的基础矩阵
write.table(out,file="expTime.txt",sep="\t",row.names=F,quote=F)
rt=read.table("expTime.txt", header=T, sep="\t", check.names=F, row.names=1) #读取输入文件
##单因素cox分析
outTab=data.frame()
sigGenes=c("futime","fustat")
for(gene in colnames(rt[,3:ncol(rt)])){
set.seed(123456)
cox=coxph(Surv(futime, fustat) ~ rt[,gene], data = rt)
coxSummary = summary(cox)
coxP=coxSummary$coefficients[,"Pr(>|z|)"]
if(coxP<pFilter){
sigGenes=c(sigGenes,gene)
outTab=rbind(outTab,
cbind(gene=gene,
HR=coxSummary$conf.int[,"exp(coef)"],
HR.95L=coxSummary$conf.int[,"lower .95"],
HR.95H=coxSummary$conf.int[,"upper .95"],
pvalue=coxP))
print(coxP)
}
}
#输出单因素结果
write.table(outTab,file="uniCox_gpr.txt",sep="\t",row.names=F,quote=F)
surSigExp=rt[,sigGenes]
surSigExp=cbind(id=row.names(surSigExp),surSigExp)
write.table(surSigExp,file="uniSigExp_gpr.txt",sep="\t",row.names=F,quote=F)
#### 热图
##########热图
#正常和肿瘤数目、
#load('STAD_tpm.Rdata')
gene=read.table('uniCox_gpr.txt',header = T)
gene=gene$gene
data=KIRC_tpm[gene,]
group=sapply(strsplit(colnames(data),"\\-"), "[", 4)
group=sapply(strsplit(group,""), "[", 1)
group=gsub("2", "1", group)
conNum=length(group[group==1]) #正常组样品数目
treatNum=length(group[group==0]) #肿瘤组样品数目
sampleType=ifelse(group=='1',1,2)
identical(colnames(data),colnames(KIRC_tpm))
#基因差异分析
sigVec=c()
allDiff=read.csv('allDiff.csv',header = T,row.names = 1)
alldiff_cox=allDiff[gene,]
pvalue=alldiff_cox$adj.P.Val
Sig=ifelse(pvalue<0.001,"***",ifelse(pvalue<0.01,"**",ifelse(pvalue<0.05,"*","")))
sigVec=paste0(gene, Sig)
## 另起一个compare矩阵，避免破坏原来的
compare=data
# 修饰一下行名
row.names(compare)=sigVec
#调整顺序，保证出图美观
normal=compare[,sampleType==1]
tumor=compare[,sampleType==2]
compare=cbind(normal,tumor)
#热图可视化
Type=c(rep("Normal",conNum), rep("Tumor",treatNum))
names(Type)=colnames(compare)
Type=as.data.frame(Type)
library(pheatmap)
pheatmap::pheatmap(compare,
#color = colorRampPalette(c(rep("blue",5), "white", rep("red",5)))(100),
color=colorRampPalette(c("navy", "white", "firebrick3"))(100),
annotation=Type,
breaks = c(seq(-3,3,length.out = 100)),
cluster_cols =F,
cluster_rows =T,
scale="row",
show_colnames=F,
show_rownames=T,
fontsize=6,
fontsize_row=7,
fontsize_col=6)
################先lasso,进行bootstrap_multicox回归#####################
gene=read.table('uniCox_gpr.txt',header = T)
gene=gene$gene
# 没有包先安装
#install.packages('survival')
library(survival)
library(survminer)
rt=read.table("expTime.txt", header=T, sep="\t", check.names=F, row.names=1) #读取输入文件
rt=rt[,c('futime','fustat',gene)]
## 先lasso筛基因!!!!!!!
set.seed(202209) #设定随机种子
rt <- rt[rt$futime>30,]
x=as.matrix(rt[,c(3:ncol(rt))])
y=data.matrix(Surv(rt$futime,rt$fustat))
# 没包先安装
# install.packages('glmnet')
library(glmnet)
fit=glmnet(x, y, family = "cox", alpha = 1)
plot(fit, xvar = "lambda", label = F)
cvfit = cv.glmnet(x, y, family="cox",nfolds = 10,alpha=1)
plot(cvfit)
#其中两条虚线分别指示了两个特殊的λ值
abline(v = log(c(cvfit$lambda.min,cvfit$lambda.1se)),lty="dashed")
coef =coef(fit,s = cvfit$lambda.min)
index = which(coef !=0)
actCoef = coef[index]
lassoGene = row.names(coef)[index]
geneCoef = cbind(Gene=lassoGene,Coef=actCoef)
geneCoef #查看模型的相关系数
## 剩下基因
gene=read.table('uniCox_gpr.txt',header = T)
gene=gene[gene$gene %in% geneCoef[,1],]
write.table(gene,file = 'uniCox_lasso_gpr.txt',quote = F,sep = '\t',row.names = F)
########进行bootstrap_multicox回归####
#install.packages('boot')
library(boot)
gene=read.table('uniCox_lasso_gpr.txt',header = T)
# boot_coef=coef/Boot_sd
gene=gene$gene
library(survival)
library(survminer)
rt=read.table("expTime.txt", header=T, sep="\t", check.names=F, row.names=1) #读取输入文件
rt=rt[,c('futime','fustat',gene)]
rt <- rt[rt$futime>30,]
# 初始HR
cox=coxph(Surv(futime, fustat) ~.,data = rt)
ggforest(cox)
#install.packages('boot')
library(boot)
rsq <- function(formula, data, indices) {
d <- data[indices,]
fit <- coxph(formula, data=d)
return(fit$coefficients)
}
## bootstrap，稍等待
set.seed(123456)
boot_results <- boot(data=rt, statistic=rsq,
R=1000, formula=Surv(futime, fustat) ~ .)
## 单因素不良预后的这边可能变好，但是因为是多因素，不管
print(boot_results)
## 获取参数
coef=boot_results$t0
sd=as.data.frame(boot_results$t)
sd=apply(sd, 2, sd)
## 定义coef/sd为新的参数
ratio=coef/sd
GPR=data.frame('Coef'=coef,'boot_SD'=sd,'Coef\\/boot_SD'=ratio)
head(GPR)
write.csv(GPR,file= 'GPR_coef.csv',quote = F)
# 构建GPRscore
gene=read.table('uniCox_lasso_gpr.txt',header = T)
gene=gene$gene
data=KIRC_tpm[gene,]
#删掉正常样品
group=sapply(strsplit(colnames(data),"\\-"),"[",4)
group=sapply(strsplit(group,""),"[",1)
group=gsub("2","1",group)
data=data[,group==0]
# 读取系数
coef=read.csv('GPR_coef.csv',header = T,check.names = F)
coef=coef$Coef..boot_SD
gpr_score=c()
for (i in 1:ncol(data)) {
score=sum(as.numeric(data[,i])*coef)
gpr_score=c(gpr_score,score)
}
data[1:4,1:4]
data=as.data.frame(t(data))
data$gpr_score=gpr_score
#读取生存数据
#suv=read.table('TCGA-STAD.survival.tsv',row.names = 1,header = T,check.names = F)
colnames(kirc.sinfo)
cli=dplyr::select(kirc.sinfo,'survival_time','fustat')
colnames(cli)=c("futime", "fustat")
head(cli)
rownames(cli) <- paste0(rownames(cli),"-01A")
##数据合并并输出结果
sameSample=intersect(row.names(data),row.names(cli))
data=data[sameSample,]
cli=cli[sameSample,]
## K-M生存分析
rt=cbind(cli,data)
rt$futime=rt$futime/30
#rt <- rt[rt$futime>0,]
library(survival)
library(survminer)
### 中位值划分
Type=ifelse(data[,'gpr_score']<= median(rt$gpr_score), "Low", "High")
data=rt
data$group=Type
data$group=factor(data$group, levels=c("Low", "High"))
save(data,file = "~/TCGA/exosome_ccRCC/tcgasub.rds")
diff=survdiff(Surv(futime, fustat) ~ group, data = data)
length=length(levels(factor(data[,"group"])))
pValue=1-pchisq(diff$chisq, df=length-1)
fit <- survfit(Surv(futime, fustat) ~ group, data = data)
bioCol=c("#0073C2","#EFC000","#6E568C","#7CC767","#223D6C","#D20A13","#FFD121","#088247","#11AA4D")
bioCol=bioCol[1:length]
p=ggsurvplot(fit,
data=data,
conf.int=F,
pval=pValue,
pval.size=6,
legend.title='GPR_score',
legend.labs=levels(factor(data[,"group"])),
legend = c(0.88, 0.9),
font.legend=12,
xlab="Time(Months)",
palette = bioCol,
surv.median.line = "hv",
risk.table=T,
cumevents=F,
risk.table.height=.25)
p
save(data,file ='GPRscore_and_group.Rdata')
View(sub)
##ROC 和time ROC
#c-index
library(pROC)
library(timeROC)
riskRoc <- timeROC(T = sub$futime/12,delta = sub$fustat,
marker = sub$gpr_score,cause = 1,
weighting="marginal",
times = c(0.5,1,2,3,5))
multiTimeplot <- function(ROC,time,cex,xlab,ylab,title){
library(ggsci)
color <- pal_lancet()(length(time))
plot(ROC$FP[,1], ROC$TP[,1], type="l", xlim=c(0,1), ylim=c(0,1),
col=color[1],
xlab=xlab,
ylab=ylab,main=title)
#如果直接plot roc对象，无法修改标题和坐标轴标签
for(i in 2:length(time)){
plot(ROC,time=time[i],add=T,col=color[i])
}
legend("bottomright",
legend =paste("AUC at",time,"year:",round(ROC$AUC,digits = 4)),
col = color,lwd = 1,
bty = "n",cex = cex,text.col = color
)
}
multiTimeplot(riskRoc,time = c(0.5,1,2,3,5),
title="Time dependent ROC curve",
xlab="False positive rate",
ylab="True positive rate",
cex=0.7)
riskRoc #验证绘图结果
####在多个数据集中进行验证####
####japan####
load("/home/data/vip39/database/KIRC_immune_therapy/japan_cohort/japan_RCCall.rds")
library(boot)
gene=read.table('uniCox_lasso_gpr.txt',header = T)
# boot_coef=coef/Boot_sd
gene=gene$gene
library(survival)
library(survminer)
class(japan_mRNA_fpkm)
japan.expr2 <- as.data.frame(t(japan_mRNA_fpkm[gene,]))
colnames(japan_clin)
japan.cli <- japan_clin[,c("month","outcome")]
colnames(japan.cli) <- c("futime", "fustat")
comsam <- intersect(rownames(japan.expr2),rownames(japan.cli))
japan.expr2 <- japan.expr2[comsam,]
japan.cli <- japan.cli[comsam,]
rt2 <- cbind(japan.cli,japan.expr2)
rt2$fustat <- ifelse(rt2$fustat=="alive",0,1)
#rt=read.table("expTime.txt", header=T, sep="\t", check.names=F, row.names=1) #读取输入文件
rt2=rt2[,c('futime','fustat',gene)]
#rt <- rt[rt$futime>30,]
# 初始HR
head(rt2)
cox=coxph(Surv(futime, fustat) ~.,data = rt2)
ggforest(cox)
#install.packages('boot')
library(boot)
rsq <- function(formula, data, indices) {
d <- data[indices,]
fit <- coxph(formula, data=d)
return(fit$coefficients)
}
## bootstrap，稍等待
set.seed(123456)
boot_results <- boot(data=rt2, statistic=rsq,
R=1000, formula=Surv(futime, fustat) ~ .)
## 单因素不良预后的这边可能变好，但是因为是多因素，不管
print(boot_results)
## 获取参数
coef=boot_results$t0
sd=as.data.frame(boot_results$t)
sd=apply(sd, 2, sd)
## 定义coef/sd为新的参数
ratio=coef/sd
GPR_japan=data.frame('Coef'=coef,'boot_SD'=sd,'Coef\\/boot_SD'=ratio)
write.csv(GPR_japan,file= 'GPR_coef_japan.csv',quote = F)
# 构建GPRscore
# 读取系数
coef=read.csv('GPR_coef_japan.csv',header = T,check.names = F)
coef=GPR_japan
coef=coef$Coef..boot_SD
class(rt2)
colnames(rt2)
data <- as.data.frame(t(rt2[,-c(1,2)]))
gpr_score=c()
for (i in 1:ncol(data)) {
score=sum(as.numeric(data[,i])*coef)
gpr_score=c(gpr_score,score)
}
data[1:4,1:4]
data=as.data.frame(t(data))
data$gpr_score=gpr_score
#读取生存数据
## K-M生存分析
rt2$gpr_score <- data$gpr_score
rt2$futime
#rt <- rt[rt$futime>0,]
library(survival)
library(survminer)
### 中位值划分
Type=ifelse(data[,'gpr_score']<= median(rt2$gpr_score), "Low", "High")
data=rt2
data$group=Type
data$group=factor(data$group, levels=c("Low", "High"))
diff=survdiff(Surv(futime, fustat) ~ group, data = data)
length=length(levels(factor(data[,"group"])))
pValue=1-pchisq(diff$chisq, df=length-1)
fit <- survfit(Surv(futime, fustat) ~ group, data = data)
bioCol=c("#0073C2","#EFC000","#6E568C","#7CC767","#223D6C","#D20A13","#FFD121","#088247","#11AA4D")
bioCol=bioCol[1:length]
p=ggsurvplot(fit,
data=data,
conf.int=F,
pval=pValue,
pval.size=6,
legend.title='GPR_score',
legend.labs=levels(factor(data[,"group"])),
legend = c(0.88, 0.9),
font.legend=12,
xlab="Time(Months)",
palette = bioCol,
surv.median.line = "hv",
risk.table=T,
cumevents=F,
risk.table.height=.25)
p
riskRoc <- timeROC(T = rt2$futime/12,delta = rt2$fustat,
marker = rt2$gpr_score,cause = 1,
weighting="marginal",
times = c(0.5,1,2,3,5))
multiTimeplot <- function(ROC,time,cex,xlab,ylab,title){
library(ggsci)
color <- pal_lancet()(length(time))
plot(ROC$FP[,1], ROC$TP[,1], type="l", xlim=c(0,1), ylim=c(0,1),
col=color[1],
xlab=xlab,
ylab=ylab,main=title)
#如果直接plot roc对象，无法修改标题和坐标轴标签
for(i in 2:length(time)){
plot(ROC,time=time[i],add=T,col=color[i])
}
legend("bottomright",
legend =paste("AUC at",time,"year:",round(ROC$AUC,digits = 4)),
col = color,lwd = 1,
bty = "n",cex = cex,text.col = color
)
}
multiTimeplot(riskRoc,time = c(0.5,1,2,3,5),
title="Time dependent ROC curve",
xlab="False positive rate",
ylab="True positive rate",
cex=0.7)
riskRoc #验证绘图结果
ROC <- timeROC(T = rt2$futime/12,
delta = rt2$fustat,
marker = rt2$gpr_score,
cause = 1,
weighting = "marginal",
times = c(1,2,3,5),
iid = TRUE)
ROC
df_plot <- data.frame(tpr = as.numeric(ROC$TP),
fpr = as.numeric(ROC$FP),
year = rep(c("1-year","2-year","3-year","5-year"),each = nrow(ROC$TP)))
head(df_plot)
library(ggplot2)
p <- ggplot(df_plot, aes(fpr, tpr, color = year)) +
geom_smooth(se=FALSE, size=1.2)+ # 这就是平滑曲线的关键
geom_abline(slope = 1, intercept = 0, color = "grey10",linetype = 2) +
scale_color_manual(values = c("#E41A1C","#377EB8","#4DAF4A","#6A3D9AFF"),
name = NULL,
labels = c(paste0("AUC at 1 year: ",round(ROC[["AUC"]][1],2)),
paste0("AUC at 2 year: ",round(ROC[["AUC"]][2],2)),
paste0("AUC at 3 year: ",round(ROC[["AUC"]][3],2)),
paste0("AUC at 5 year: ",round(ROC[["AUC"]][4],2)))
) +
coord_fixed(ratio = 1) +
labs(x = "1 - Specificity", y = "Sensitivity") +
theme_minimal(base_size = 14, base_family = "sans") +
theme(legend.position = c(0.7,0.15),
panel.border = element_rect(fill = NA),
axis.text = element_text(color = "black"))
p
##DCA
as.numeric(str_sub(japan_clin[rownames(rt2),"Stage"],3,3))
rt2$Stage <- as.numeric(factor(str_split_fixed(KIRC_cli[rownames(rt2),"pathologic_stage"]," ",2)[,2]))
rt2$Stage <-as.numeric(str_sub(japan_clin[rownames(rt2),"Stage"],3,3))
rt2$Fuhrman <- japan_clin[rownames(rt2),"Fuhrman"]
rt2$Age <- japan_clin[rownames(rt2),"Age"]
rt2$cancer <- rt2$fustat==1
rt2$ttcancer <- rt2$futime/12 ##年份
##取出不要的数据
rt2 <- rt2[rt2$Fuhrman!="undetermined",]
rt2$Fuhrman <- as.numeric(rt2$Fuhrman)
# 建立多个模型
Risk_score <- coxph(Surv(ttcancer, cancer) ~ gpr_score,
data = rt2)
Stage <- coxph(Surv(ttcancer, cancer) ~ Stage, data = rt2)
Age <- coxph(Surv(ttcancer, cancer) ~ Age, data = rt2)
Fuhrman <- coxph(Surv(ttcancer, cancer) ~ Fuhrman, data = rt2)
df3 <- ggDCA::dca(Risk_score,Stage,Age,
times = c(1,2,3,5)
)
ggplot(df3,linetype = F)+
scale_color_jama(name="Model Type")+
theme_bw()+
facet_wrap(~time)
##准备候选基因和临床信息 标准输入矩阵即可
###cancer cell ####
load("/home/data/vip39/database/KIRC_immune_therapy/cancercellpaper/cancercellRCC.rds")
table(Cancercell_cli$ARM)
# atezo_bev sunitinib
# 407 416
table(Cancercell_cli$OBJECTIVE_RESPONSE)
# CR NE PD PR SD
# 32 65 151 257 318
#完全缓解（CR）
#部分缓解（PR）
#疾病进展（PD）
#病稳定（SD）
#无法评估 （NE）
#控制率CR+PR+SD
32+318+257
Sunitinib_arm <- Cancercell_cli[Cancercell_cli$ARM=="sunitinib",]
Sunitinib_arm$risk_score <- rt3[rownames(Sunitinib_arm),"gpr_score"]
Sunitinib_arm <- Sunitinib_arm[Sunitinib_arm$OBJECTIVE_RESPONSE!="NE",]
Sunitinib_arm$outcome <- ifelse(Sunitinib_arm$OBJECTIVE_RESPONSE=="PD", "Poor","Good")
##ROC
# 先来之前的
library(pROC)
roc1 <- roc(Sunitinib_arm$outcome, Sunitinib_arm$risk_score,data=Sunitinib_arm,aur=TRUE,
levels=c("Good", "Poor"),smooth=T,ci=T,
boot.n=100)
plot(roc1,print.auc=T,ci=T)
# 计算sensitivity(se)的CI,根据100次bootstrap，每0.1取个点
se.obj <- ci(roc1, of="se", boot.n=100,specificities=seq(0, 1, 0.1))
plot(se.obj,type='bars',col = 'black',print.auc=T)
## 获取矩阵
roc1_df=data.frame(TPR=roc1$sensitivities,FPR=1-roc1$specificities)
roc1_df=roc1_df[order(roc1_df$TPR,decreasing = T),]
se.obj=as.data.frame(se.obj)
se.obj$FPR=1-as.numeric((rownames(se.obj)))
se.obj$TPR=se.obj$`50%`
g <- ggplot() +
geom_line(data=roc1_df,aes(x =FPR, y = TPR),size=0.6,color='#0072b5') +
geom_line(aes(x=c(0,1),y=c(0,1)),color = "grey",size = 1,linetype=6 )+
labs(x = "False positive rate", y = "Ture positive rate", title ="ROC curve")+
geom_point(aes(x =FPR, y = TPR), size=3,data =
se.obj,shape=21,color='white',stroke=2,fill='#0072b5') +
geom_errorbar(aes(ymin=`2.5%`,ymax=`97.5%`,x=FPR,y=TPR),data = se.obj,
width=.03,size=1,color='#0072b5')+
annotate("text",x = .75, y = .25,label = paste("Riskscore
AUC=",round(roc1$auc,2)),color = "#0073c2",size=4)+
theme_bw()
g
atezo_bev_arm <- Cancercell_cli[Cancercell_cli$ARM=="atezo_bev",]
atezo_bev_arm$risk_score <- rt3[rownames(atezo_bev_arm),"gpr_score"]
##ROC
atezo_bev_arm <- atezo_bev_arm[atezo_bev_arm$OBJECTIVE_RESPONSE!="NE",]
atezo_bev_arm$outcome <- ifelse(atezo_bev_arm$OBJECTIVE_RESPONSE=="PD", "Poor","Good")
atezo_bev_arm$risk_group <- ifelse(atezo_bev_arm$risk_score>median(atezo_bev_arm$risk_score),"high","low")
##ROC
# 先来之前的
library(pROC)
roc1 <- roc(atezo_bev_arm$outcome, atezo_bev_arm$risk_score,data=atezo_bev_arm,aur=TRUE,
levels=c("Good", "Poor"),smooth=T,ci=T,
boot.n=100)
plot(roc1,print.auc=T,ci=T)
# 计算sensitivity(se)的CI,根据100次bootstrap，每0.1取个点
se.obj <- ci(roc1, of="se", boot.n=100,specificities=seq(0, 1, 0.1))
plot(se.obj,type='bars',col = 'black',print.auc=T)
## 获取矩阵
roc1_df=data.frame(TPR=roc1$sensitivities,FPR=1-roc1$specificities)
roc1_df=roc1_df[order(roc1_df$TPR,decreasing = T),]
se.obj=as.data.frame(se.obj)
se.obj$FPR=1-as.numeric((rownames(se.obj)))
se.obj$TPR=se.obj$`50%`
g <- ggplot() +
geom_line(data=roc1_df,aes(x =FPR, y = TPR),size=0.6,color='#0072b5') +
geom_line(aes(x=c(0,1),y=c(0,1)),color = "grey",size = 1,linetype=6 )+
labs(x = "False positive rate", y = "Ture positive rate", title ="ROC curve")+
geom_point(aes(x =FPR, y = TPR), size=3,data =
se.obj,shape=21,color='white',stroke=2,fill='#0072b5') +
geom_errorbar(aes(ymin=`2.5%`,ymax=`97.5%`,x=FPR,y=TPR),data = se.obj,
width=.03,size=1,color='#0072b5')+
annotate("text",x = .75, y = .25,label = paste("Riskscore
AUC=",round(roc1$auc,2)),color = "#0073c2",size=4)+
theme_bw()
g
##sunitinib 组进行预测
##atezo_bev组进行预测
rt_cancercell <- as.data.frame(t(Cancercell_tpm[gene,]))
colnames(Cancercell_cli)
Cancercell_cli$futime <- as.numeric(Cancercell_cli$PFS_MONTHS)*30
Cancercell_cli$fustat <- ifelse(Cancercell_cli$PFS_CENSOR=="TRUE",1,0)
cancercell_cli <- Cancercell_cli[,c("futime","fustat")]
comsam <- intersect(rownames(rt_cancercell),rownames(cancercell_cli))
rt_cancercell <- rt_cancercell[comsam,]
cancercell_cli <- cancercell_cli[comsam,]
rt3 <- cbind(cancercell_cli,rt_cancercell)
#rt=read.table("expTime.txt", header=T, sep="\t", check.names=F, row.names=1) #读取输入文件
rt3=rt3[,c('futime','fustat',gene)]
#rt <- rt[rt$futime>30,]
# 初始HR
cox=coxph(Surv(futime, fustat) ~.,data = rt3)
ggforest(cox)
library(boot)
rsq <- function(formula, data, indices) {
d <- data[indices,]
fit <- coxph(formula, data=d)
return(fit$coefficients)
}
## bootstrap，稍等待
set.seed(123456)
boot_results <- boot(data=rt3, statistic=rsq,
R=1000, formula=Surv(futime, fustat) ~ .)
## 单因素不良预后的这边可能变好，但是因为是多因素，不管
print(boot_results)
## 获取参数
coef=boot_results$t0
sd=as.data.frame(boot_results$t)
sd=apply(sd, 2, sd)
## 定义coef/sd为新的参数
ratio=coef/sd
GPR_cancercell=data.frame('Coef'=coef,'boot_SD'=sd,'Coef\\/boot_SD'=ratio)
write.csv(GPR_cancercell,file= 'GPR_coef_cancercell.csv',quote = F)
# 读取系数
coef=read.csv('GPR_coef_cancercell.csv',header = T,check.names = F)
coef <- GPR_cancercell
coef=coef$Coef..boot_SD
class(rt3)
colnames(rt3)
data <- as.data.frame(t(rt3[,-c(1,2)]))
gpr_score=c()
for (i in 1:ncol(data)) {
score=sum(as.numeric(data[,i])*coef)
gpr_score=c(gpr_score,score)
}
data[1:4,1:4]
data=as.data.frame(t(data))
data$gpr_score=gpr_score
#读取生存数据
## K-M生存分析
rt3$gpr_score <- data$gpr_score
rt3$futime
#rt <- rt[rt$futime>0,]
library(survival)
library(survminer)
### 中位值划分
Type=ifelse(data[,'gpr_score']<= median(rt3$gpr_score), "Low", "High")
data=rt3
data$group=Type
data$group=factor(data$group, levels=c("Low", "High"))
diff=survdiff(Surv(futime, fustat) ~ group, data = data)
length=length(levels(factor(data[,"group"])))
pValue=1-pchisq(diff$chisq, df=length-1)
fit <- survfit(Surv(futime, fustat) ~ group, data = data)
bioCol=c("#0073C2","#EFC000","#6E568C","#7CC767","#223D6C","#D20A13","#FFD121","#088247","#11AA4D")
bioCol=bioCol[1:length]
p=ggsurvplot(fit,
data=data,
conf.int=F,
pval=pValue,
pval.size=6,
legend.title='GPR_score',
legend.labs=levels(factor(data[,"group"])),
legend = c(0.88, 0.9),
font.legend=12,
xlab="Time(Months)",
palette = bioCol,
surv.median.line = "hv",
risk.table=T,
cumevents=F,
risk.table.height=.25)
p
library(timeROC)
riskRoc <- timeROC(T = rt3$futime/12,delta = rt3$fustat,
marker = rt3$gpr_score,cause = 1,
weighting="marginal",
times = c(0.5,1,2,3,5))
multiTimeplot <- function(ROC,time,cex,xlab,ylab,title){
library(ggsci)
color <- pal_lancet()(length(time))
plot(ROC$FP[,1], ROC$TP[,1], type="l", xlim=c(0,1), ylim=c(0,1),
col=color[1],
xlab=xlab,
ylab=ylab,main=title)
#如果直接plot roc对象，无法修改标题和坐标轴标签
for(i in 2:length(time)){
plot(ROC,time=time[i],add=T,col=color[i])
}
legend("bottomright",
legend =paste("AUC at",time,"year:",round(ROC$AUC,digits = 4)),
col = color,lwd = 1,
bty = "n",cex = cex,text.col = color
)
}
multiTimeplot(riskRoc,time = c(0.5,1,2,3,5),
title="Time dependent ROC curve",
xlab="False positive rate",
ylab="True positive rate",
cex=0.7)
riskRoc #验证绘图结果
ROC <- timeROC(T = rt3$futime/12,
delta = rt3$fustat,
marker = rt3$gpr_score,
cause = 1,
weighting = "marginal",
times = c(1,2,3,5),
iid = TRUE)
ROC
df_plot <- data.frame(tpr = as.numeric(ROC$TP),
fpr = as.numeric(ROC$FP),
year = rep(c("1-year","2-year","3-year","5-year"),each = nrow(ROC$TP)))
head(df_plot)
library(ggplot2)
p <- ggplot(df_plot, aes(fpr, tpr, color = year)) +
geom_smooth(se=FALSE, size=1.2)+ # 这就是平滑曲线的关键
geom_abline(slope = 1, intercept = 0, color = "grey10",linetype = 2) +
scale_color_manual(values = c("#E41A1C","#377EB8","#4DAF4A","#6A3D9AFF"),
name = NULL,
labels = c(paste0("AUC at 1 year: ",round(ROC[["AUC"]][1],2)),
paste0("AUC at 2 year: ",round(ROC[["AUC"]][2],2)),
paste0("AUC at 3 year: ",round(ROC[["AUC"]][3],2)),
paste0("AUC at 5 year: ",round(ROC[["AUC"]][4],2)))
) +
coord_fixed(ratio = 1) +
labs(x = "1 - Specificity", y = "Sensitivity") +
theme_minimal(base_size = 14, base_family = "sans") +
theme(legend.position = c(0.7,0.15),
panel.border = element_rect(fill = NA),
axis.text = element_text(color = "black"))
p
##校准
library(survival)
library(rms)
library(dplyr)
library(tidyr)
library(paletteer)
paletteer_d("RColorBrewer::Paired")
colnames(rt2)
rt2$futime
range(rt2$futime)
class(rt2$time)
rt2$time <- rt2$futime*30
##1年
f1 <- cph(formula = Surv(time, fustat) ~ gpr_score,data=rt2,
x=T,y=T,surv = T,na.action=na.delete,time.inc = 365)
#参数m=50表示每组50个样本进行重复计算
cal1 <- calibrate(f1, cmethod="KM", method="boot",u=365,m=30,B=1000)
##2年
f2 <- cph(formula = Surv(time, fustat) ~ gpr_score,data=rt2,
x=T,y=T,surv = T,na.action=na.delete,time.inc = 730)
#参数m=50表示每组50个样本进行重复计算
cal2 <- calibrate(f5, cmethod="KM", method="boot",u=730,m=30,B=1000)
##3年
f3 <- cph(formula = Surv(time, fustat) ~ gpr_score,data=rt2,
x=T,y=T,surv = T,na.action=na.delete,time.inc = 1095)
#参数m=50表示每组50个样本进行重复计算
cal3 <- calibrate(f3, cmethod="KM", method="boot",u=1095,m=30,B=1000)
##5年
f5 <- cph(formula = Surv(time, fustat) ~ gpr_score,data=rt2,
x=T,y=T,surv = T,na.action=na.delete,time.inc = 1825)
#参数m=50表示每组50个样本进行重复计算
cal5 <- calibrate(f5, cmethod="KM", method="boot",u=1825,m=30,B=1000)
# #8年
# f8 <- cph(formula = Surv(time, fustat) ~ gpr_score,
# data=rt2,x=T,y=T,surv = T,na.action=na.delete,time.inc = 2920)
# cal8 <- calibrate(f8, cmethod="KM", method="boot",u=2920,m=25,B=1000)
#pdf("calibration_compare.pdf",width = 8,height = 8)
plot(cal1,lwd = 2,lty = 0,errbar.col = c("#2166AC"),
bty = "l", #只画左边和下边框
xlim = c(0,1),ylim= c(0,1),
xlab = "Nomogram-prediced OS (%)",ylab = "Observed OS (%)",
col = c("#2166AC"),
cex.lab=1.2,cex.axis=1, cex.main=1.2, cex.sub=0.6)
lines(cal1[,c('mean.predicted',"KM")],
type = 'b', lwd = 1, col = c("#2166AC"), pch = 16)
mtext("")
plot(cal2,lwd = 2,lty = 0,errbar.col = c("#B2182B"),
xlim = c(0,1),ylim= c(0,1),col = c("#B2182B"),add = T)
lines(cal2[,c('mean.predicted',"KM")],
type = 'b', lwd = 1, col = c("#B2182B"), pch = 16)
plot(cal3,lwd = 2,lty = 0,errbar.col = c("#FF7F00FF"),
xlim = c(0,1),ylim= c(0,1),col = c("#FF7F00FF"),add = T)
lines(cal3[,c('mean.predicted',"KM")],
type = 'b', lwd = 1, col = c("#FF7F00FF"), pch = 16)
plot(cal5,lwd = 2,lty = 0,errbar.col = c("#6A3D9AFF"),
xlim = c(0,1),ylim= c(0,1),col = c("#6A3D9AFF"),add = T)
lines(cal5[,c('mean.predicted',"KM")],
type = 'b', lwd = 1, col = c("#6A3D9AFF"), pch = 16)
abline(0,1, lwd = 2, lty = 3, col = c("#224444"))
legend("topleft", #图例的位置
legend = c("1-year","2-year","3-year","5-year"), #图例文字
col =c("#2166AC","#B2182B","#FF7F00FF","#6A3D9AFF"), #图例线的颜色，与文字对应
lwd = 2,#图例中线的粗细
cex = 1.2,#图例字体大小
bty = "n")#不显示图例边框
#dev.off()
###美化ROC
##DCA
##cancerll DCA
colnames(Cancercell_cli)
rt3$IMDC <- Cancercell_cli[rownames(rt3),"IMDC_RISK_SCORE"]
rt3$MSKCC <- Cancercell_cli[rownames(rt3),"MSKCC_RISK_SCORE"]
rt3$Age <- as.numeric(Cancercell_cli[rownames(rt3),"AGE"])
rt3$TMB <- as.numeric(Cancercell_cli[rownames(rt3),"TMB"])
rt3$cancer <- rt3$fustat==1
rt3$ttcancer <- rt3$futime/30 ##月份
#rt3 <- rt3[rt3$TCGA_cluster!="Other",]
rt3 <- na.omit(rt3)
table(rt3$IMDC)
table(rt3$MSKCC)
rt3$IMDC <- as.numeric(factor(rt3$IMDC,levels = c("POOR","Intermediate","Favorable")))
rt3$MSKCC <- as.numeric(factor(rt3$MSKCC,levels = c("Low","Intermediate","High")))
# 建立多个模型
Risk_score <- coxph(Surv(ttcancer, cancer) ~ gpr_score,
data = rt3)
IMDC <- coxph(Surv(ttcancer, cancer) ~ IMDC, data = rt3)
MSKCC <- coxph(Surv(ttcancer, cancer) ~ MSKCC, data = rt3)
Age <- coxph(Surv(ttcancer, cancer) ~ Age, data = rt3)
TMB <- coxph(Surv(ttcancer, cancer) ~ TMB, data = rt3)
range(rt3$ttcancer)